Traffic signs are an inherent part of a traffic environment. The signs regulate the flow of the vehicles, give specific information, or warn against unexpected road circumstances. For that reason, perception and fast interpretation of signs is critical for the safety of the drivers of the vehicles. One way to do this is with a computer vision application.
The designs of traffic signs are usually to some national or international standard, e.g., the European Vienna Convention on Road Traffic treaty 1968.
A conventional sign detection methods generally use a heuristic based on available prior knowledge about traffic signs to define how to a segment images acquired of a scene to find regions of interest regions, and to define acceptable geometrical relationships between the color and shape of signs. The major deficiency of those methods is a lack of a solid theoretical foundation, and a high parametrization.
Another method uses a trainable cascade of boosted classifiers to learn the most discriminative local image descriptors for sign detection. Other methods track traffic signs over time. However most of those methods use a relatively simple scheme based on a predefined motion model and some sort of geometrical Kalman filtering.
Typically, a cross-correlation template matching technique is used for sign classification. Other methods involve neural networks, or kernel density estimation.
Other sign detection and recognition methods are described in the following U.S. Pat. No. 7,466,841, Method for traffic sign detection, U.S. Pat. No. 6,813,545—Automatic traffic sign recognition, U.S. Pat. No. 6,801,638,—Device and method for recognizing traffic signs, and U.S Applications 20080137908—Detecting and recognizing traffic signs, 20060034484—Method for traffic sign detection, and 20040010352—Automatic traffic sign recognition, incorporated herein by reference.